is used to increase the diversity of training data, e.g., translation, cropping, which help a model learn the main features by altering the input patterns. To find out the best combination for a variety of datasets, AutoAugment [1]} and RandAugment [2]} are designed to search for a better combination. These augmentation strategies are proved to be transferable to different datasets.
